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59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
# This config is used for pass@k evaluation with dataset repetition
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# That model cannot generate multiple response for single input
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from mmengine.config import read_base
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from opencompass.models import HuggingFaceCausalLM
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from opencompass.partitioners import SizePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.tasks import OpenICLInferTask
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with read_base():
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from opencompass.configs.datasets.humaneval.humaneval_repeat10_gen_8e312c import \
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humaneval_datasets
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from opencompass.configs.datasets.mbpp.deprecated_mbpp_repeat10_gen_1e1056 import \
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mbpp_datasets
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from opencompass.configs.datasets.mbpp.deprecated_sanitized_mbpp_repeat10_gen_1e1056 import \
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sanitized_mbpp_datasets
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datasets = []
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datasets += humaneval_datasets
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datasets += mbpp_datasets
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datasets += sanitized_mbpp_datasets
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_meta_template = dict(round=[
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dict(role='HUMAN', begin='<|User|>:', end='\n'),
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dict(role='BOT', begin='<|Bot|>:', end='<eoa>\n', generate=True),
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], )
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models = [
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dict(
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abbr='internlm-chat-7b-hf-v11',
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type=HuggingFaceCausalLM,
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path='internlm/internlm-chat-7b-v1_1',
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tokenizer_path='internlm/internlm-chat-7b-v1_1',
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tokenizer_kwargs=dict(
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padding_side='left',
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truncation_side='left',
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use_fast=False,
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trust_remote_code=True,
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),
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max_seq_len=2048,
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meta_template=_meta_template,
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model_kwargs=dict(trust_remote_code=True, device_map='auto'),
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generation_kwargs=dict(
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do_sample=True,
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top_p=0.95,
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temperature=0.8,
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),
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run_cfg=dict(num_gpus=1, num_procs=1),
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batch_size=8,
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)
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]
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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=600),
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runner=dict(type=LocalRunner,
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max_num_workers=16,
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task=dict(type=OpenICLInferTask)),
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)
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